In this paper, we define trimmed videos as pre-segmented video clips that each contains only one action instance. In other words, the context of the action, i.e., moments before or after the action are not included in the video. Therefore, action detection in trimmed videos only need to classify the action categories without the need to detect starting and ending timestamps. Recognizing actions in trimmed videos has many applications in video surveillance, robotics, medical diagnosis [1]}, and has achieved excellent performance in recent years [2]}, [3]}, [4]}.
However, the majority of videos in the wild, i.e., recorded in unconstrained environments, are naturally untrimmed. Untrimmed videos are lengthy unsegmented videos that may include several action instances, the moments before or after each action, and the transition from one action to another. The action instances in one video can belong to several action classes and have different duration.
